Search Results (1,120)

Extreme rainfall poses an increasing threat to mountainous transportation systems by frequently triggering landslides along road corridors. Most existing studies focus on long-term landslide susceptibility, whereas event-scale assessments remain limited, particularly in road environments. This study develops an event-scale framework for assessing landslide risk along mountain roads under extreme rainfall conditions, using the July 2023 “23·7” rainfall event in Mentougou District, Beijing, as a case study. A Random Forest model was constructed by integrating multi-source geospatial data with an event-specific inventory of 8930 landslides. The model achieved high predictive performance, with ROC–AUC values of 0.9187 and 0.9166 for the validation and test datasets, respectively. Feature importance analysis further indicates that landslide occurrence is controlled by the combined effects of rainfall, terrain conditions, vegetation cover, and anthropogenic disturbance, with rainfall acting as the primary trigger. High-risk road segments are mainly concentrated in the southeastern part of the study area, showing clear spatial clustering. These results highlight the value of event-scale analysis and demonstrate the effectiveness of the road-oriented framework for identifying hazardous segments under extreme rainfall conditions. The proposed approach provides practical support for landslide monitoring, risk mitigation, and resilient management of mountainous transportation infrastructure. Full article

Background and Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder in which gait and balance disturbances substantially increase the risk of falls and loss of independence. Pharmacological treatment alleviates several motor symptoms but has limited effects on postural instability. Backward walking (BW), a demanding locomotor task, has recently been investigated as both an assessment tool and a rehabilitation strategy in PD. The purpose of this focused systematic review is to analyse the benefits and limitations of retro walking in relation to the gait parameters and balance control of PD patients. Materials and Methods: A structured literature search (2015–2025) was conducted across multiple databases in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines. Eligibility criteria, screening procedures, and qualitative synthesis methods were predefined. Nine studies (including two randomized controlled trials) met the inclusion criteria. Methodological quality was assessed using PEDro and ROBINS-I tools, and the certainty of evidence was evaluated using GRADE. Results: The research results indicate within-group improvements in balance and gait parameters following BW training. Some of the included studies also suggest that BW may be a sensitive marker of balance deficits and fall risk. However, the evidence is limited by small sample sizes, heterogeneity of interventions, and a predominance of non-randomized designs. Conclusions: Current evidence regarding BW in PD remains preliminary. While BW may be considered as a supplementary component of rehabilitation, its specific efficacy cannot be clearly distinguished from general exercise effects. Further high-quality randomized controlled trials with standardized protocols and long-term follow-up are required. Full article

In complex marine environments, achieving low-cost, highly reliable, and continuous navigation is crucial for the intelligent and autonomous operation of unmanned surface vehicles (USVs). Currently, the integrated Global Navigation Satellite System and Strapdown Inertial Navigation System (GNSS/SINS) serves as the primary navigation architecture for USVs. While the cost of high-performance GNSS receivers has steadily decreased, high-precision SINS remains prohibitively expensive. Consequently, micro-electromechanical system (MEMS)-based SINS has emerged as a preferred alternative due to its favorable balance of cost, power consumption, and size. However, significant inertial sensor errors make it difficult to maintain high-precision positioning during GNSS outages. To address this limitation, the single-axis rotational inertial navigation system (SRINS) has been introduced. Nevertheless, constrained by the single-axis mechanical structure and complex sea state disturbances, the system still struggles to effectively modulate random errors and azimuth gyroscope drift, rendering it insufficient for highly demanding navigation tasks. To overcome these bottlenecks, this article systematically reviews four core technologies: (1) Comprehensive denoising and temperature drift compensation techniques for MEMS gyroscopes; (2) rapid moving-base initial alignment models under high sea state disturbances; (3) fast online calibration methods for azimuth gyroscope drift; and (4) adaptive and robust GNSS/SINS integration architectures capable of accommodating high-dynamic conditions and non-Gaussian interference. Finally, this article discusses the engineering conflict between deploying high-precision algorithms and the limited onboard computational capacity of USVs. It concludes by highlighting a highly promising navigation paradigm for future research: the integration of factor graph optimization with physics-informed deep learning. Full article

This study examines the evolving structural relationship between capital networks and technological trajectories in China’s artificial intelligence (AI) industry from 2018 to 2023. Using a network-based analytical framework, we integrate venture capital co-investment data with patent-text semantic similarity measures to assess the structural association between financial connectivity and technological distribution patterns. Technological diversity is quantified using text-embedding techniques and Shannon entropy, while Quadratic Assignment Procedure (QAP) models are employed to evaluate inter-network alignment between capital ties and technological similarity. The results indicate a progressively strengthened capital–technology coupling accompanied by increasing technological convergence within the industrial network. Robustness checks across multiple similarity thresholds confirm the stability of these structural associations. Quadrant-based analysis identifies a persistent asymmetry between technologically distinctive but financially peripheral firms and highly central yet technologically homogeneous actors. Robustness analysis further suggests a “robust yet fragile” network configuration characterized by resilience to random disturbances but vulnerability to hub-targeted shocks. Collectively, the findings illuminate the structural implications of capital–technology interdependence for industrial sustainability. From a sustainability perspective, maintaining structural diversity alongside capital coordination is essential for preserving adaptive capacity in rapidly evolving innovation ecosystems. Excessive alignment between financial networks and dominant technological paradigms may enhance short-term efficiency but constrain long-term evolutionary flexibility. Full article

Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), narcolepsy, REM sleep behavior disorder, and circadian rhythm disturbances, represent a rapidly expanding global health burden that is strongly associated with cardiovascular, metabolic, neurological, and psychiatric diseases. Advancements in wearable sensing technologies and Internet of Medical Things (IoMT) infrastructures have expanded the possibilities for continuous, home-based sleep assessment beyond conventional polysomnography laboratories. These Sleep Health Internet of Things (S-HIoT) systems combine multimodal physiological sensing (EEG, ECG, SpO₂, respiratory effort and actigraphy) with wireless communication and cloud-based analytics for automated sleep-stage classification and disorder detection. Nonetheless, the digitization of sleep medicine brings about significant cybersecurity concerns. The constant transmission of sensitive biomedical information makes S-HIoT networks open to anomalous traffic flows, signal manipulation, replay attacks, spoofing, and data integrity violation. Existing studies mostly focus on analyzing physiological signals and network intrusion detection independently, resulting in a systemic vulnerability of cyber–physical sleep monitoring ecosystems. With the aim of addressing this empirical deficiency, this review integrates emerging advances (2022–2026) in the AI-assisted categorization of sleep phases and IoMT anomaly detector designs on the finer analysis of CNN, LSTM/BiLSTM, Transformer-based systems, and a component part of federated schemes and the lightweight, edge-deployable intruder assessor models available. The aim of this study is to uncover a gap in the literature: integrated architectures to trade off audiences of faithfulness of physiological modeling with communication-layer security. To counter it, we present a single framework to include CNN-based spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM)-based temporal models and Random Forest-based ensemble classification using a dual task-learning approach. We propose a multi-objective optimization framework to jointly optimize the performance of sleep-stage prediction and that of network anomaly detection. Performance on publicly available datasets (Sleep-EDF and CICIoMT2024) confirms that hybrid integration can be tailored to achieve high accuracy [99.8% sleep staging; 98.6% anomaly detection] whilst being characterized by low inference latency (<45 ms), which is promising for feasibility in real-time deployment in view of targeting edge devices. This work presents a comprehensive framework for developing secure, intelligent, and clinically robust digital sleep health ecosystems by bridging chronobiological signal modeling with cybersecurity mechanisms. Furthermore, it highlights future research directions, including explainable AI, federated secure learning, adversarial robustness, and energy-aware edge optimization. Full article

Background/Objectives: The objective of this study was to map and synthesize the current evidence on hemostasis in chronic and acute liver disease within the framework of Patient Blood Management (PBM). Methods: Because research in this field is heterogeneous—spanning mechanistic studies, observational data, randomized controlled trials, guidelines, and expert reviews—a scoping review was selected to comprehensively map concepts. Findings were synthesized narratively to reflect the breadth and heterogeneity of available research. Results: Hemostasis in liver disease is characterized by a fragile state of rebalanced coagulation, where parallel reductions in pro- and anticoagulant factors coexist with variable fibrinolytic disturbances and thrombocytopenia. Conventional coagulation tests (CCTs) do not accurately reflect bleeding risk, whereas viscoelastic assays and thrombomodulin-modified thrombin generation testing provide a more physiologic assessment, though with limitations. Most bleeding events arise from portal hypertension rather than coagulopathy, and the routine prophylactic correction of abnormal results of CCTs is not supported by evidence. PBM-aligned strategies—such as restrictive transfusion, targeted fibrinogen replacement, and use of thrombopoietin receptor agonists (TPO-RAs)—reduce unnecessary blood product use. Thrombosis burden is increasingly recognized in this patient population. Anticoagulation is generally safe when individualized to liver function and clinical context, however significant variability persists in clinical practice, and high-quality data remain limited for advanced disease. Conclusions: Hemostasis in liver disease reflects a dynamic and unstable equilibrium rather than a simple bleeding tendency. Diagnostic and therapeutic strategies grounded in PBM principles improve safety by avoiding unnecessary transfusion and emphasize individualized care. Despite advances in understanding rebalanced hemostasis, major gaps remain in predicting thrombotic risk, standardizing advanced coagulation testing, and defining optimal management across disease stages. Full article

Background: Magnesium regulates neuronal excitability, NMDA receptor activity, and cerebrovascular tone. Dysmagnesemia is common in patients with acute brain injury (>65%), yet large randomized trials of magnesium neuroprotection have been neutral despite strong physiological rationale and consistent observational associations with outcomes. A key limitation may be diagnostic misclassification: the total serum magnesium poorly reflects the biologically active ionized fraction and may misclassify magnesium status in 20–85% of ICU patients during critical illness. Purpose: This narrative review synthesizes current evidence on magnesium physiology, measurement limitations, and clinical implications in neurocritical care. Overview: We discuss the mechanisms of magnesium depletion, outline the conceptual “two-hit” model (chronic deficiency plus acute ICU losses), and highlight the potential value of ionized magnesium for improved patient evaluation. Emerging syndrome-specific data suggest that magnesium disturbances are associated with prognostic signals. Improved phenotyping may help explain prior trial neutrality and support stratified approaches to magnesium monitoring and repletion. Future studies should evaluate magnesium-guided strategies and phenotype-driven trials to clarify the therapeutic role of magnesium in neurocritical care. Full article

Background/Objectives: Major depressive disorder (MDD) remains a leading cause of disability worldwide and exhibits substantial biological heterogeneity that is not adequately captured by current symptom-based diagnostic systems. While the classical monoamine hypothesis has historically guided antidepressant development, it does not fully account for variability in treatment response, delayed therapeutic onset, or the persistence of cognitive and anhedonic symptoms. Converging evidence from molecular, neuroimaging, and translational studies increasingly implicates glutamatergic dysregulation and impaired neuroplasticity as key mechanisms in depressive pathology. This narrative review aims to integrate monoaminergic and glutamatergic perspectives within a dimensional framework that may help explain clinical heterogeneity and inform mechanism-based treatment strategies. Methods: A narrative synthesis of the literature was conducted using major biomedical databases including PubMed, Scopus, and Web of Science. Preclinical studies, neuroimaging investigations, biomarker research, randomized clinical trials, and meta-analyses examining monoaminergic dysfunction, glutamatergic signaling, neuroplasticity pathways, and rapid-acting antidepressants were reviewed and thematically integrated. Results: Evidence indicates that depressive syndromes may reflect varying contributions of monoaminergic dysregulation and glutamatergic–neuroplastic impairment. Monoaminergic disturbances interact with inflammatory and neuroendocrine processes, including cytokine-driven activation of the kynurenine pathway. In parallel, alterations in glutamatergic signaling, glial function, and BDNF–TrkB–mTOR pathways contribute to synaptic atrophy and network dysfunction. Rapid-acting antidepressants such as ketamine, esketamine, and dextromethorphan–bupropion provide clinical proof-of-concept that direct engagement of synaptic plasticity mechanisms can accelerate symptom improvement, particularly in treatment-resistant depression. Conclusions: Integrating monoaminergic and glutamatergic mechanisms within a “monoamine–glutamate continuum” offers a conceptual framework for understanding depressive heterogeneity and treatment response. Multimodal approaches combining clinical phenotyping with inflammatory, neuroimaging, and molecular markers may ultimately support mechanism-informed precision psychiatry strategies in major depressive disorder. Full article

Invasive alien species such as Lantana camara L. impact native species and soil properties, but context-specific effects in transfrontier conservation areas remain poorly understood. Understanding these effects is essential for biodiversity conservation and management. We assessed associations between L. camara presence and native woody species composition and structure, as well as soil nutrients, in protected and communal areas within the Kavango–Zambezi Transfrontier Conservation Area (KAZA TFCA), Zimbabwe. The study hypothesised that invasion effects on vegetation are stronger in communal areas due to higher disturbance, and that soil changes are influenced by land-use intensity. We used stratified random sampling to select 60 plots across invaded and uninvaded sites. Woody vegetation was assessed for species composition and richness, stem density, canopy cover %, height, and diameter at breast height. Soil samples were analysed for nitrogen, organic carbon, phosphorus, potassium, and pH. The presence of L. camara was negatively associated with native species richness, density, height, and canopy cover %, with stronger effects in communal plots. Invaded plots had lower pH (e.g., 6.1 in Park areas) and higher levels of some soil nutrients, particularly phosphorus and organic carbon, though patterns varied by land-use type. These results suggest that anthropogenic disturbance amplifies invasion impacts. We conclude that L. camara reduces native vegetation diversity and structure in this species-rich transfrontier area. Management should prioritise control at communal edges to support woody species resilience, ecosystem services, and biodiversity, with strategies adapted to local land-use conditions. Full article

In the parallel operation of islanded microgrids, line impedance mismatches and random load fluctuations, along with the dynamic response lag and difficulty in multidimensional parameter tuning of traditional control strategies, lead to power sharing imbalances and instability in frequency and voltage. To address these issues, this paper proposes a hierarchical cooperative control strategy based on consistency that integrates load-observation-based dynamic reference feedforward (LODRF) and adaptive particle swarm optimization (APSO). First, an improved adaptive virtual impedance (IAVI) strategy based on consistency is introduced into the virtual synchronous generator control framework. Second, an LODRF mechanism is applied at the secondary control layer to actively reconstruct the power baseline by observing the load status at the point of common coupling (PCC) in real time. Furthermore, an APSO algorithm utilizing the integral of time-weighted absolute error (ITAE) as a global performance index is constructed to optimize key proportional–integral controller parameters cooperatively. Simulation results from a four-unit heterogeneous parallel system in MATLAB/Simulink demonstrate that the IAVI strategy enables stable convergence of frequency and voltage and proportional power sharing. Compared with the system without LODRF, the proposed strategy reduces maximum frequency and voltage dynamic deviations under load disturbances by 78.5% and 53.3%, respectively, and shortens effective recovery times by 0.01 s and 0.09 s, respectively. Moreover, compared with the standard PSO algorithm, the APSO-optimized system reduces maximum frequency and voltage deviations by 3.1% and 36.4%, respectively. Additionally, average active and reactive power sharing errors in the steady state are kept below 0.9%, verifying the significant advantages of the strategy in improving dynamic disturbance rejection and steady-state precision. Full article

Background: Psychological determinants are increasingly recognized as central contributors to both performance decline and recovery in competitive sport; however, contemporary evidence integrating injury-related and non-injury performance contexts remains fragmented. Objective: This systematic review synthesized empirical evidence (2016–2025) examining psychological determinants associated with return to sport (RTS), reinjury risk, burnout, injury incidence, and performance decline among competitive athletes. Methods: Conducted in accordance with PRISMA 2020 guidelines, a systematic search of PubMed, Scopus, Web of Science, and SPORTDiscus identified peer-reviewed studies published between January 2016 and December 2025. Eligibility criteria were defined using a PICO framework. Prospective cohort studies, longitudinal multi-wave investigations, one randomized controlled trial, matched cohort studies, diary-based designs, and injury-related observational studies were included. Due to heterogeneity in constructs and outcomes, findings were synthesized narratively. Results: Fourteen studies met the inclusion criteria, including prospective cohort studies, multi-wave longitudinal designs, one randomized controlled trial, one matched cohort study, and a diary-based investigation. Seven independent cohorts examined psychological readiness using the Anterior Cruciate Ligament—Return to Sport after Injury scale (ACL-RSI) in athletes with anterior cruciate ligament (ACL) injuries (sample sizes ranging from n = 39 to n = 384), consistently demonstrating that higher readiness predicted successful RTS at 6–24 months, while two prospective studies reported contrasting associations with second ACL injury risk. Four longitudinal studies (n = 93–491) showed that increased burnout and controlled motivation predicted performance decline and dropout trajectories, whereas higher resilience and mental toughness reduced burnout progression. One seasonal longitudinal study (n = 21) linked elevated cognitive anxiety and mood disturbance to increased injury incidence. Conclusion: Psychological determinants operate across deterioration and restoration pathways. Psychological readiness shows the strongest predictive consistency for RTS, while burnout, motivational climate, and resilience significantly shape long-term performance sustainability and injury-related outcomes. Full article

The uptake and accumulation of mercury (Hg) in rice poses a serious threat to food safety. Pot experiments are widely used to screen for low-Hg-accumulating cultivars, yet their reliability in predicting field performance remains uncertain. This study evaluated pot-based screening by (1) comparing Hg uptake in rice grown in freshly processed versus aged soil; (2) contrasting Hg accumulation in the same cultivars grown in pots versus at two field sites; and (3) isolating micro-environmental effects by burying pots in situ. A total of 22 rice cultivars were used during 2021–2023 in this study. Pot systems, regardless of soil treatment, failed to replicate field accumulation patterns, yielding significantly greater Hg concentrations in brown rice (up to 59.24 ng g⁻¹) than field conditions (maximum 32.33 ng g⁻¹). Cultivar rankings derived from pot experiments showed little or no correlation with field rankings, indicating that performance is not transferable across environments. Random forest analysis identified elevated soil temperature and reduced light intensity as key artificial factors driving overestimation in pots, explaining 15.68% (total Hg) and 21.65% (methylmercury) of the variation. We conclude that pot experiments—due to soil disturbance and altered microclimates—overestimate Hg accumulation potential and show limited predictive capacity under the tested conditions. Therefore, field validation across multiple sites and seasons is essential for accurate mercury risk assessment and region-specific cultivar recommendation. Full article

Leakage identification in district heating networks is challenging because leakage-induced transient pressure waves often overlap with pressure disturbances triggered by routine operations such as valve regulation, pump speed variation, and emergency shut-off. In addition, the scarcity of high-quality labeled leakage samples limits the robustness of data-driven models under small-sample conditions. To address these issues, this study proposes a compact and moderately interpretable framework for multi-condition identification from transient pressure-wave signals, integrating signal preprocessing, handcrafted statistical feature extraction, multiclass ReliefF-based feature selection, and class-wise generative adversarial network augmentation in the selected feature space. A dataset containing four representative conditions, namely leakage, valve regulation, pump speed regulation, and emergency valve shut-off, was constructed using an integrated indoor district heating network testbed. After Hampel-based spike suppression and zero-phase Butterworth band-pass filtering within 0.5 to 300 Hz, time- and frequency-domain statistical features were extracted, and a compact subset was selected by multiclass ReliefF. A class-wise generative adversarial network was then used to augment the training set in feature space, while all evaluations were performed strictly on real samples. The results show that feature-space augmentation improves robustness and generalization under operational disturbances and noise. Using random forest as the representative classifier, Accuracy and Macro-F1 increased from 0.960 to 0.985, while leakage recall improved from 0.920 to 0.980. Further comparisons confirmed that the ReliefF-selected subset outperformed representative alternatives such as LASSO and mRMR. Overall, the proposed framework provides an effective solution for distinguishing leakage events from operational disturbances and offers practical support for online monitoring and intelligent operation of district heating networks. Full article

Minimizing the impacts of coupling, randomness, time variation and uncertain nonlinearity to enhance real-time performance is critical for controlling complex industrial systems. This paper proposes an optimized adaptive filtering method (LAF-MTNF) for time-varying uncertain nonlinear systems with multiple-input multiple-output (MIMO) measurement noise, which integrates the multi-dimensional Taylor network (MTN) with Lyapunov stability theory (LST). Leveraging MTN’s inherent advantages—simple structure, linear parameterization, and low computational complexity—LAF-MTNF achieves efficient real-time filtering while avoiding the exponential computation burden of neural networks. The contributions of this work are threefold: (1) A novel integration of LST and MTN is proposed for MIMO filtering, in which an energy space is constructed with a unique global minimum to eliminate local optimization traps, addressing the stability deficit of traditional MTN filters using LMS/RLS algorithms. (2) Convergence performance is systematically quantified by deriving explicit expressions for the error convergence rate (regulated by a positive constant) and convergence region (a sphere centered at the origin) while modifying adaptive gain to avoid singularity, filling the gap of incomplete performance analysis in existing Lyapunov-based filters. (3) The design is disturbance-independent, relying only on input/output measurements and requiring no prior knowledge of noise statistics, thus enhancing robustness to unknown industrial disturbances. We systematically analyze the Lyapunov stability of LAF-MTNF, and simulations on a complex MIMO system verify that it outperforms existing methods in filtering precision (mean error 0.0227 vs. 0.0674 of RBFNN) and dynamic response speed, while ensuring asymptotic stability and real-time applicability. The proposed LAF-MTNF method achieves significant advantages over traditional adaptive filtering methods in filtering accuracy, convergence speed and anti-cross-coupling capability. This method has broad application prospects in high-precision industrial servo motion control, power system state monitoring and other multi-variable nonlinear industrial scenarios with complex noise environments. Full article

To address the challenges of constrained grid-like compartments, a motion framework integrating adaptive obstacle avoidance planning and active disturbance rejection control is proposed. First, an Adaptive Rapidly exploring Random Tree Star (Adaptive RRT*) algorithm based on multi-source state feedback is developed. Scaled-down model simulations show that, compared to conventional algorithms, its path length (374.28 mm), planning time (0.30 s), and node count (50.83) are reduced by at least 29.5%, 64.7%, and 28.6%, respectively, achieving a 100% planning success rate. Next, a control scheme based on Extended State Observer–Model Predictive Control (ESO-MPC) is designed. Simulations indicate that under nominal conditions, tracking errors are reduced by 5.78–84.35% compared to traditional MPC. Under a 20% link mass perturbation, the scheme effectively eliminates phase lag. Under complex scenarios involving parameter perturbation and a 0.6 N·m step torque disturbance, the tracking error reduction ranges from 25.27% to 87.59%, exhibiting excellent disturbance rejection robustness. Physical experiments conducted on a scaled-down experimental platform further verify that the maximum tracking errors of the manipulator end-effector along the x, y, and z axes under ESO-MPC are 0.88 mm, 0.85 mm, and 0.89 mm, respectively, significantly outperforming the 2.41 mm, 2.39 mm, and 2.47 mm observed with MPC. Finally, obstacle avoidance and trajectory-tracking simulations of an industrial manipulator in a full-scale ship compartment environment validate the engineering feasibility of the proposed framework. Full article